- Title
- Forecasting plug-in electric vehicles load profile using artificial neural networks
- Creator
- Panahi, Delshad; Deilami, Sara; Masoum, Mohammad; Islam, Syed
- Date
- 2015
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/167312
- Identifier
- vital:13655
- Identifier
-
https://doi.org/10.1109/AUPEC.2015.7324879
- Identifier
- ISBN:978-147998725-2
- Abstract
- Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading. An artificial neural network (ANN) model will be developed to forecast daily arrival time (Ta) and daily travel distance (Dtr) of individual PEV using historical data collected for each vehicle in the past two years. The predicted parameters are then will be used to forecast transformer loading with PEV charging activities. The results of this paper will be very beneficial to coordination and charge/discharge management of PEVs as well as demand load management, network planning and operation proposes. Detailed simulations are presented to investigate the feasibility and accuracy of the proposed forecasting strategy.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 25th Australasian Universities Power Engineering Conference, AUPEC 2015; Wollongong, Australia; 27th-30th September 2015 p. 1-6
- Rights
- Copyright © 2015 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Forecasting; Load profile; Neural network and distribution transformer loading; Plug-in electric vehicles
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